We have been very pleased, beyond our expectations, with the reception of
the first edition of this book. Bioinformatics, however, continues to evolve
very rapidly, hence the need for a new edition. In the past three years, fullgenome
sequencing has blossomed with the completion of the sequence of
the fly and the first draft of the Human Genome Project. In addition, several
other high-throughput/combinatorial technologies, such as DNA microarrays
and mass spectrometry, have considerably progressed. Altogether, these highthroughput
technologies are capable of rapidly producing terabytes of data
that are too overwhelming for conventional biological approaches. As a result,
the need for computer/statistical/machine learning techniques is today
stronger rather than weaker.
An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.
